There is a strong relationship between mass and terminal velocity squared because the correlation coefficient of the linear fit of its graph is .9902. The mass portion of this relationship derives from number of filters because each filter adds the same amount of mass, but the drag force model -bv appears to model the data better because the line matches the graph better. Incorporating acceleration due to gravity does not change it because the acceleration remains the same when determining terminal velocity and terminal velocity squared. If one filter falls in time t, then four filters would fall in 0.25t since there is a direct relationship between the number of filters, an indication of mass, and terminal velocity. The teminal velocity at four filters would be 4vT, or 4 meters per second. However, we are looking at seconds not meters, so the time would be 0.25t.
There is a strong relationship between mass and terminal velocity squared because the correlation coefficient of the linear fit of its graph is .9902. The mass portion of this relationship derives from number of filters because each filter adds the same amount of mass, but the drag force model -bv appears to model the data better because the line matches the graph better. Incorporating acceleration due to gravity does not change it because the acceleration remains the same when determining terminal velocity and terminal velocity squared. If one filter falls in time t, then four filters would fall in 0.25t since there is a direct relationship between the number of filters, an indication of mass, and terminal velocity. The teminal velocity at four filters would be 4vT, or 4 meters per second. However, we are looking at seconds not meters, so the time would be 0.25t.